glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression

Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.

Version: 0.5.1
Depends: R (≥ 3.6.0)
Imports: MASS, car, stats, ggplot2, ggpubr, glmmTMB, graphics, lme4, lmerTest, methods, plotly, qvalue, pbapply, pbmcapply
Suggests: knitr, rmarkdown, kableExtra, DESeq2, edgeR, emmeans
Published: 2022-09-12
Author: Myles Lewis ORCID iD [aut, cre], Katriona Goldmann ORCID iD [aut], Elisabetta Sciacca ORCID iD [aut], Cankut Cubuk ORCID iD [ctb], Anna Surace ORCID iD [ctb]
Maintainer: Myles Lewis <myles.lewis at>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-gb
Materials: README NEWS
CRAN checks: glmmSeq results


Reference manual: glmmSeq.pdf
Vignettes: glmmSeq


Package source: glmmSeq_0.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): glmmSeq_0.5.1.tgz, r-oldrel (arm64): glmmSeq_0.5.1.tgz, r-release (x86_64): glmmSeq_0.5.1.tgz, r-oldrel (x86_64): glmmSeq_0.5.1.tgz
Old sources: glmmSeq archive


Please use the canonical form to link to this page.